Responsibilities Research and validate machine learning approaches for DAS-based vessel detection Train and adapt deep learning and computer vision models for acoustic signal interpretation Build proof‑of‑concept AI systems integrating DAS and AIS datasets Develop scalable ML workflows using Vertex AI and MLFlow Analyze vessel signatures including engine, propeller, and vibration patterns Collaborate with cross‑functional teams to deliver production‑oriented R&D outcomes Present findings, experiments, and validation results to technical stakeholders Requirements Strong experience training and fine‑tuning deep learning models in PyTorch Hands‑on experience with computer vision and transformer‑based architectures such as DINO / Vision Transformers Experience building ML pipelines and experiment tracking using MLFlow and Vertex AI Strong Python engineering skills and modern AI development workflows Experience working with time‑series or signal processing data Comfortable operating in ambiguous R&D environments with evolving requirements Nice‑to‑have: Experience with DAS, seismic, sonar, or other sensor‑based systems Familiarity with PostgreSQL/PostGIS and geospatial indexing (H3) Experience deploying ML systems on Google Cloud Platform Understanding of acoustic signal analysis and spectral methods Experience with multimodal data fusion (vision + sensor data) #J-18808-Ljbffr
